Department of Mechanical Engineering, IIT Madras, Chennai 600036, India.
Comput Biol Med. 2011 Sep;41(9):771-9. doi: 10.1016/j.compbiomed.2011.06.013. Epub 2011 Jul 12.
Tumor-site-specific delivery of anti-cancer drugs remains one of the most prevailing problems in cancer treatment. While conventional means of chemo-delivery invariably leave different degrees of side-effects on healthy tissues, in recent times, intelligent chemical designs have been exploited to reduce the cross-consequences. In particular, the strategies involving superparamaganetic nanoparticles with surface assembled oligonucleotides as therapeutic carrier have raised affirmative promises. Process is designed in such a way that the therapeutic molecules are released preferentially at target site as the complementary oligonucleotide chains dissociate over the heat generated by the nanoparticles under the excitation of low frequency electromagnetic energy. In spite of the preliminary demonstrations, analytical comprehension of the entire process especially on the purview of non-trivial interactions between stochastic phase-transition phenomena of oligonucleotide chains and hierarchical organization of in vivo transport processes remains unknown. Here, we propose an integrated computational predictive model to interpret the efficacy of drug delivery in the aforementioned process. The basic physics of heat generation by superparamagnetic nanoparticles in presence of external electromagnetic field has been coupled with transient biological heat transfer model and the statistical mechanics based oligonucleotide denaturation dynamics. Conjunctionally, we have introduced a set of hierarchically appropriate transport processes to mimic the in vivo drug delivery system. The subsequent interstitial diffusion and convection of the various species involved in the process over time was simulated assuming a porous media model of the carcinoma. As a result, the model predictions exhibit excellent congruence with available experimental results. To delineate a broader spectrum of a priori speculations, we have investigated the effects of different tunable parameters such as magnetizing field strength, nanoparticle size, diffusion coefficients, porous media parameters and different oligonucleotide sequences on temperature rise and site-specific drug release. The proposed model, thus, provides a generic framework for the betterment of nanoparticle mediated drug delivery, which is expected to impart significant impact on cancer therapy.
肿瘤部位特异性递药仍然是癌症治疗中最普遍的问题之一。虽然传统的化疗药物递送方法不可避免地会对健康组织造成不同程度的副作用，但近年来，人们已经利用智能化学设计来减少这种交叉影响。特别是，涉及表面组装寡核苷酸作为治疗载体的超顺磁性纳米粒子的策略已经带来了积极的前景。该过程的设计方式是，当互补的寡核苷酸链在纳米粒子产生的热量下解离时，治疗分子优先在靶部位释放，而纳米粒子在低频电磁场的激发下产生热量。尽管已经进行了初步的演示，但对整个过程的分析理解，特别是对寡核苷酸链的随机相变现象与体内输运过程的层次结构之间的非平凡相互作用的分析理解，仍然未知。在这里，我们提出了一个综合的计算预测模型，以解释上述过程中药物递释的效果。在存在外电磁场的情况下，超顺磁纳米粒子产生热量的基本物理原理与瞬态生物传热模型和基于统计力学的寡核苷酸变性动力学相结合。同时，我们引入了一组层次适当的输运过程来模拟体内药物递释系统。假设癌组织为多孔介质模型，模拟了各种物种在不同时间的间质扩散和对流。结果表明，模型预测与现有实验结果具有极好的一致性。为了阐明更广泛的先验推测，我们研究了不同可调参数（如磁化场强度、纳米粒子大小、扩散系数、多孔介质参数和不同的寡核苷酸序列）对温度升高和肿瘤部位特异性药物释放的影响。因此，所提出的模型为改善基于纳米粒子的药物递释提供了一个通用框架，有望对癌症治疗产生重大影响。
